Robotic Process Automation (RPA), a fast emerging AI-based technology, has been identified as a game changer for Asia Pacific organisations with nearly half (46%) of all CIOs in the region currently investing in or planning to invest in RPA to automate highly repeatable, highly structured tasks across business software systems, according to the 2017 Harvey Nash/KPMG CIO Survey. But while RPA has often been cited as offering operational and cost benefits, many companies struggle identifying which of their operational aspects can leverage on – and benefit from – this technology.
In an email interview with Networks Asia, Daniel Peled, General Manager for Asia Pacific at Kryon System, discusses the low-down on RPA and its integral role in digital transformation and talks about how organisations can accurately and efficiently identify business processes that can be automated by RPA.
How does RPA replace or supplement digital transformation? What of enterprises efforts to go digital thus far?
Digital transformation is here, and it is already impacting the way business is done and the way organisations work. However, majority are still stuck with antiquated business processes and legacy systems that are unable to deliver on new levels of business agility and efficiency that define today’s modern enterprise. As organisations examine ways of digitising their business functions, robotic process automation (RPA) technology stands out for its ability to impact business outcomes with significant and rapid results, thus accelerating an organisation’s digital transformation efforts.
In Asia Pacific, digitising systems and automating processes through the implementation of artificial intelligence (AI) technologies has emerged as a key component in digital transformation strategies, with IDC identifying AI/Cognitive systems as a foundation for digital transformation. Furthermore, 70% of enterprises across Asia Pacific are expected to use AI Services by 2021, with regional cognitive and artificial spending predicted to reach $1.0 billion in 2018 alone. Already, enterprises across key APAC industries – banking, retail, and healthcare – are realising how leveraging RPA and cognitive AI can drive efficiency, cost reduction and risk management.
The banking ($140.7 million), retail ($112.7 million) and healthcare ($87.6 million) industries are expected to be the 2018 biggest cognitive/AI spenders in Asia Pacific, according to IDC. Asia Pacific banks, for instance, are seen to leverage AI for various use cases, including automated customer service agents, fraud analysis and investigation, and IT automation. Introducing automated customer service agents in particular has been a priority for many banks, which are constantly on the lookout for ways to elevate the customer experience and service levels. A study by Accenture has found that eight in 10 Singaporeans are open to robo-advisory services for their banking, insurance and retirement planning, presenting an opportunity for banks to capitalise on.
How does RPA fit into AI or machine learning? If it’s highly repeatable and automated, how does it differ from some form of process automation or middleware? What is done with the data generated to make it fit into AI norms?
The most sophisticated RPA technologies and solutions often incorporate AI and machine learning to automate complex processes more efficiently and reliably, significantly helping organisations boost business process efficiency, prevent human errors and generate cost reductions.
Beyond the capabilities of most middleware and process automation, RPA can easily integrate into systems – which range from legacy to modern IT environments – through non-invasive means. Advanced RPA technologies only collect the data on user actions to identify and recommend which processes can be automated for time and cost savings.
How would RPA implementation fit into an enterprise’s existing digital transformation plans? Should it be viewed as a project on its own, or an add-on to the transformation?
RPA has identified as a game changing technology for digital transformation in Asia Pacific with nearly half (46%) of all CIOs in the region currently investing in or planning to invest in RPA to automate highly repeatable, highly structured tasks across business software systems, according to the 2017 Harvey Nash/KPMG CIO Survey.
However, a common mistake many organisations make when embarking on their digital transformation journeys is not having a holistic view of their IT environments as well as the lack of knowledge of what businesses processes can be digitised. To streamline digital transformation efforts and the adoption of technologies such as RPA, organisations should work with vendors who are able to efficiently map out which aspects of the business will yield the most returns once digitised, thus helping organisations realise their digital transformation goals at a faster pace.
When it comes to IoT, machine learning and m2m communications, are we looking at the development of deeper learning or augmented intelligence that we can use for better machine learning? How far off are we from automated intelligence or artificial intelligence?
Machine learning has seen significant advances in recent years, and deep learning is one of the most important reasons for this progress. Machine learning systems now have vastly improved and are able to process information, get feedback, and learn at a much quicker pace compared to older systems.
As more strides to technologies such as AI are made, we can definitely expect to see more intelligent automation to save valuable worktime, cut operational costs, and reduce the probability of human errors, which can translate to exorbitant costs.
What about generating information from data? Are we making more or better sense from what data we have or are we fumbling in the dark? How can enterprises take better advantage of the data they are generating?
As organisations start digitising their business models, many are often inundated with the amount of information flowing through their network daily and struggle to make sense of the data. To address this, companies need to take the first step in learning how to better manage their data – and this can be done through the adoption of RPA.
At a time when the ability to handle data quickly and accurately has never been more important, RPA will be able to take on the laborious task of data gathering and data entry, while significantly eliminating human error. A comprehensive RPA solution offers control of access privileges to ensure maintenance of data privacy and security. This comes at a time when legislations such as the General Data Protection Regulation (GDPR) are causing headaches for many organisations as they struggle to maintain compliance, especially with the increasing volume of data they hold.
How much of what we’re hearing from vendors around AI/AR and RPA or machine learning (ML) is hype and how much is reality? When does one go from ML to RPA to AI? We’ve been hearing about reactive IT for a while now, what is different now? Who is leading in this area?
A common myth surrounding AI and RPA is the oversimplification that these technologies or ‘robots’ will replace humans in the workforce. However, while these progressive technologies will definitely disrupt the workplace, they should not be viewed as a threat but as an opportunity that employees can leverage to augment their capabilities, efficiency and work performance.
The potential of using AI and RPA to optimise business processes in the workplace shows no signs of slowing down, especially in Asia Pacific, a hotbed for digital innovation. Organisations in the Asia Pacific region are exploring how to best leverage business process automation and digitisation to bridge gaps in functional silos, enhance customer experiences, and streamline operations. A report by Gartner has revealed that 37% CIOs in Asia Pacific have deployed or are in short-term planning for AI deployment (compared to 25% globally).
Organisations will naturally turn to technology such as AI, machine learning and RPA as these technologies are able to automate highly repeatable and structured tasks, thus speeding up their digital transformation efforts.
What is the difference between true AI and ML and rules-based engines? How about narrow-focus AI? How much application does AI have within an enterprise? How automated can we make systems that are machine learning capable? Are we ready to embrace automated IT?
Machine learning and rules-based engines can be seen as a subset of AI, although both have their strengths and weaknesses. A rules-based engine attempts to mimic the decision process of an expert or best practice by following set rules, whereas machine learning focuses on historic data – outcomes already identified by experts.
The use cases, as well as level of implementation, of AI within organisations vary depending on several factors, including employees’ and departments’ readiness. In today’s increasingly digitalised world, organisations are under immense pressure to digitise and automate all aspects of the business to achieve operational efficiency. And with automation promising streamlined operations, better customer experiences and narrower gaps in functional silos, organisations across Asia Pacific are more ready than ever to embrace automation within their operations.